Systems and methods for displaying estimated relevance indicators for result sets of documents and for displaying query visualizations

ABSTRACT

Systems and methods for displaying estimated relevance indicators for result sets of documents and for displaying query visualizations are disclosed. A method includes receiving a search query including a plurality of query terms. The method further includes searching a database using the search query to identify the result set of documents and calculating an estimated relevance score for the result set of documents. The estimated relevance score is indicative of a degree to which the result set of documents are relevant to the search query. The method further includes providing for display the estimated relevance indicator based on the estimated relevance score. The estimated relevance indicator provides a visual indication of the degree to which the result set of documents are relevant to the search query. Query visualizations including a plurality of nodes and a plurality of connectors are also disclosed.

BACKGROUND

Field

The present specification generally relates to search queries and, moreparticularly, to systems and methods for calculating estimated relevancescores of result sets of documents and for displaying estimatedrelevance indicators for result sets of documents based on thecalculated estimated relevance scores, and to systems and methods fordisplaying query visualizations.

Technical Background

Users construct search queries to search document databases (e.g., legaldocument databases, patent document databases, news article documentdatabases, financial document databases, etc.) in order to identifydocuments that satisfy a search objective. There is a risk that usersmay construct ineffective search queries that yield unsatisfactoryresult sets that do not satisfy the user's search objective. A user maymake poor or uninformed conclusions or decisions by erroneously relyingon unreliable search results. A user may also waste significant timereviewing unsatisfactory results, which may not even include thedocuments that are most relevant to the user's search objective. A usermay have no idea as to the relevance or usefulness of a set of documentsidentified by a search query until the user spends a significant amountof time and effort reviewing the documents. Furthermore, it may bedesirable to visualize and manipulate a search query in an intuitive anduser friendly manner.

Accordingly, a need exists for systems and methods for displayingestimated relevance indicators for result sets of documents and systemsand methods for displaying query visualizations.

SUMMARY

In one embodiment, a method for providing for display an estimatedrelevance indicator for a result set of documents includes receiving, ata computer, a search query including a plurality of query terms. Themethod further includes searching a database using the search query toidentify the result set of documents. The result set of documents areidentified based on the search query. The method further includescalculating an estimated relevance score for the result set ofdocuments. The estimated relevance score is indicative of a degree towhich the result set of documents are relevant to the search query. Themethod further includes providing for display the estimated relevanceindicator based on the estimated relevance score. The estimatedrelevance indicator provides a visual indication of the degree to whichthe result set of documents are relevant to the search query.

In another embodiment, a method for providing for display an estimatedrelevance indicator for a result set of documents includes receiving, ata computer, the search query. The search query includes a plurality ofquery terms. The method further includes searching a database using thesearch query to identify a result set of documents. The result set ofdocuments are identified based on the search query. The method furtherincludes calculating an estimated relevance score for the result set ofdocuments. The estimated relevance score is indicative of a degree towhich the result set of documents are relevant to the search query. Themethod further includes providing for display an estimated relevanceindicator based on the estimated relevance score. The estimatedrelevance indicator provides a visual indication of the degree to whichthe result set of documents are relevant to the search query. The methodfurther includes determining that the estimated relevance score is lessthan a relevance score threshold and providing at least one suggestionfor improving the search query in response to determining that theestimated relevance score is less than the relevance score threshold.

In yet another embodiment, a method for displaying a visualization of asearch query includes providing for display a graphical user interfacehaving a query input element. The method further includes receiving asearch query entered into the query input element. The search queryincludes a plurality of query terms. The method further includesproviding for display on the graphical user interface a queryvisualization and manipulation element including a plurality of nodesand a plurality of connectors. The plurality of nodes correspond to theplurality of query terms. Each connector of the plurality of connectorsconnects a pair of the plurality of nodes and is representative of aproximity of the corresponding query terms of the connected pair ofnodes.

These and additional features provided by the embodiments describedherein will be more fully understood in view of the following detaileddescription, in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the subject matter defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, wherein like structure is indicated with likereference numerals and in which:

FIG. 1 depicts a schematic illustration of a computing network for asystem for performing the functions described herein, according to oneor more embodiments shown and described herein;

FIG. 2 depicts a schematic illustration of the server computing devicefrom FIG. 1, further illustrating hardware and software that may beutilized to perform the functions described herein, according to one ormore embodiments shown and described herein;

FIG. 3 depicts a flowchart graphically illustrating a method offacilitating the search of a document database, according to one or moreembodiments shown and described herein;

FIG. 4 depicts a schematic illustration of a graphical user interfaceincluding a query input element, a query visualization and manipulationelement, an estimated relevance element, and a results feedback element,according to one or more embodiments shown and described herein;

FIG. 5 depicts a schematic illustration of a graphical user interfaceillustrating a query improved by adding synonyms and variants to one ofthe query terms and the resulting improvement in estimated relevance ofthe result set, according to one or more embodiments shown and describedherein;

FIG. 6 depicts a schematic illustration of a graphical user interfaceillustrating a query improved by adding an additional query term to thesearch query and the resulting improvement in estimated relevance of theresult set, according to one or more embodiments shown and describedherein;

FIG. 7 depicts a schematic illustration of a graphical user interfaceillustrating a query improved by selecting a practice group and theresulting improvement in estimated relevance, according to one or moreembodiments shown and described herein; and

FIG. 8 depicts a schematic illustration of a graphical user interfaceillustrating a query improved by changing the proximity connectors amongquery terms and the resulting improvement in estimated relevance,according to one or more embodiments shown and described herein.

DETAILED DESCRIPTION

Referring generally to the figures, embodiments described herein aredirected to systems and methods for calculating estimated relevancescores of result sets of documents that are identified based on a searchquery and to systems and methods for displaying query visualizations.Some embodiments of the systems and methods described herein alsodisplay estimated relevance indicators for result sets of documentsbased on the calculated estimated relevance scores. The displayedestimated relevance indicator provides a visual indication of the degreeto which the result set of documents are relevant to the search query.Providing a visual indication of the degree to which a result set ofdocument is relevant to a search query may allow a user to formulateimproved queries, inspect more relevant documents, and save time byreducing the amount of time spent inspecting irrelevant documents. Forexample, by providing a visual indication that a result set of documentsis estimated as of low relevance, the embodiments described herein mayalert a user that the result set is not relevant and that the usershould not waste time inspecting irrelevant results. Conversely, byproviding a visual indication that a result set of documents isestimated as of high relevance, the embodiments described herein mayalert a user that the result set is likely relevant and that the usercan proceed with inspection of the result set with confidence thatwasted time will be reduced. Additionally, providing a visual indicationthat a result set of documents is estimated as of high relevance maysignal that improvement of the search query is not required since theresults will likely be satisfactory to the user's search objective,while providing a visual indication that a result set of documents isestimated as of low relevance may signal that improvement of the searchquery is needed before inspecting the documents in the result set. Insome embodiments, at least one suggestion for improving a search queryis provided when the estimated relevance score is less than a relevancescore threshold. Some embodiments provide for display a graphical userinterface including a query input element, a query visualization andmanipulation element, an estimated relevance element, and a resultsfeedback element. Furthermore, the displayed query visualizationsdescribed herein may facilitate the visualization and manipulation of asearch query in an intuitive and user friendly manner. Variousembodiments of systems and methods for calculating estimated relevancescores of result sets of documents, for displaying estimated relevanceindicators for result sets of documents, and for displaying queryvisualizations are described below.

Although the embodiments are described herein in the context of adocument database including legal documents (e.g., cases, statutes,etc.), patent documents, news documents, financial documents, and thelike, it should be understood that embodiments are not limited thereto.

Referring now to the drawings, FIG. 1 depicts an exemplary computingnetwork, illustrating components for a system for performing thefunctions described herein, according to embodiments shown and describedherein. As illustrated in FIG. 1, a computer network 10 may include awide area network, such as the internet, a local area network (LAN), amobile communications network, a public service telephone network (PSTN)and/or other network and may be configured to electronically connect auser computing device 12 a, a server computing device 12 b, and anadministrator computing device 12 c.

The user computing device 12 a may be used to facilitate searching of adocument database, display and receive input from a graphical userinterface used to perform such searching, and display a result set ofdocuments and information pertaining to the result set of documents(e.g., an estimated relevance indicator indicative of the degree towhich the result set of documents are relevant to a search query). Theuser computing device 12 a may also facilitate the improvement of asearch query by receiving and transmitting user input in responsereceiving and displaying suggestions for improving the search query fromthe server computing device 12 b. The user computing device 12 a mayalso be utilized to perform other user functions.

The administrator computing device 12 c may, among other things, performadministrative functions for the server computing device 12 b. In theevent that the server computing device 12 b requires oversight,updating, or correction, the administrator computing device 12 c may beconfigured to provide the desired oversight, updating, and/orcorrection. The administrator computing device 12 c, as well as anyother computing device coupled to the computer network 10, may be usedto input one or more documents into the document database.

The server computing device 12 b may receive a search query from theuser computing device 12 a and search a document database using thesearch query to identify a result set of documents. The server computingdevice 12 b may also calculated an estimated relevance score for theresult set of documents that is indicative of a degree to which theresult set of documents are relevant to a search query. The servercomputing device 12 b may also transmit information to the usercomputing device 12 a such that the user computing device 12 a maydisplay the result set of documents and information pertaining to theresult set of documents, such as an estimated relevance indicator. Thecomponents and functionality of the server computing device 12 b will beset forth in detail below.

It should be understood that while the user computing device 12 a andthe administrator computing device 12 c are depicted as personalcomputers and the server computing device 12 b is depicted as a server,these are nonlimiting examples. More specifically, in some embodimentsany type of computing device (e.g., mobile computing device, personalcomputer, server, etc.) may be utilized for any of these components.Additionally, while each of these computing devices is illustrated inFIG. 1 as a single piece of hardware, this is also merely an example.More specifically, each of the user computing device 12 a, servercomputing device 12 b, and administrator computing device 12 c mayrepresent a plurality of computers, servers, databases, etc.

FIG. 2 depicts additional details regarding the server computing device12 b from FIG. 1. While in some embodiments, the server computing device12 b may be configured as a general purpose computer with the requisitehardware, software, and/or firmware, in some embodiments, that servercomputing device 12 b may be configured as a special purpose computerdesigned specifically for performing the functionality described herein.

As also illustrated in FIG. 2, the server computing device 12 b mayinclude a processor 30, input/output hardware 32, network interfacehardware 34, a data storage component 36 (which may store a documentdatabase 38 a), and a non-transitory memory component 40. The memorycomponent 40 may be configured as volatile and/or nonvolatile computerreadable medium and, as such, may include random access memory(including SRAM, DRAM, and/or other types of random access memory),flash memory, registers, compact discs (CD), digital versatile discs(DVD), and/or other types of storage components. Additionally, thememory component 40 may be configured to store operating logic 42,search logic 44, estimated relevance score calculation logic 46, displaylogic 48, and query improvement suggestion logic 49 (each of which maybe embodied as a computer program, firmware, or hardware, as anexample). A local interface 50 is also included in FIG. 2 and may beimplemented as a bus or other interface to facilitate communicationamong the components of the server computing device 12 b.

The processor 30 may include any processing component configured toreceive and execute instructions (such as from the data storagecomponent 36 and/or memory component 40). The input/output hardware 32may include a monitor, keyboard, mouse, printer, camera, microphone,speaker, touch-screen, and/or other device for receiving, sending,and/or presenting data. The network interface hardware 34 may includeany wired or wireless networking hardware, such as a modem, LAN port,wireless fidelity (Wi-Fi) card, WiMax card, mobile communicationshardware, and/or other hardware for communicating with other networksand/or devices.

It should be understood that the data storage component 36 may residelocal to and/or remote from the server computing device 12 b and may beconfigured to store one or more pieces of data for access by the servercomputing device 12 b and/or other components. As illustrated in FIG. 2,the data storage component 36 may store the document database 38 a,which in at least one embodiment includes documents that have beenorganized and indexed for searching. The document database 38 a may bestored in one or more data storage devices. In another embodiment, theserver computing device 12 b may be coupled to a remote server or datastorage device that comprises one or more of the documents in thedocument database 38 a. Other data may be stored in the data storagecomponent 36 to provide support for functionalities described herein.

Included in the memory component 40 are the operating logic 42, thesearch logic 44, the estimated relevance score calculation logic 46, thedisplay logic 48, and the query improvement suggestion logic 49. Theoperating logic 42 may include an operating system and/or other softwarefor managing components of the server computing device 12 b. Similarly,the search logic 44 may reside in the memory component 40 and may beconfigured to search the document database 38 a based on search queriesreceived from the user computing device 12 a. The estimated relevancescore calculation logic 46 may be operable to calculate an estimatedrelevance score for a result set of documents identified by searchingthe document database 38 a based on a search query received from theuser computing device 12 a. The estimated relevance score is indicativeof a degree to which the result set of documents are relevant to asearch query. The display logic 48 may facilitate the display of agraphical user interface usable by a user of the user computing device12 a to provide search queries, to display visualizations of the searchqueries, and to display of the result set of documents and informationpertaining to the result set of documents, such as an estimatedrelevance indicator. The display logic 48 may facilitate suchinformation displayed on the user computing device 12 a by transmittinginformation that is displayed by the user computing device 12 a. Thequery improvement suggestion logic 49 may generate a query improvementsuggestion that is transmitted to the user computing device 12 a fordisplay to a user. The functionalities of the operating logic 42, thesearch logic 44, the estimated relevance score calculation logic 46, thedisplay logic 48, and the query improvement suggestion logic 49 will bedescribed in further detail below.

It should be understood that the components illustrated in FIG. 2 aremerely exemplary and are not intended to limit the scope of thisdisclosure. More specifically, while the components in FIG. 2 areillustrated as residing within the server computing device 12 b, this isa nonlimiting example. In some embodiments, one or more of thecomponents may reside external to the server computing device 12 b.Similarly, while FIG. 2 is directed to the server computing device 12 b,other components such as the user computing device 12 a and theadministrator computing device 12 c may include similar hardware,software, and/or firmware.

Referring now to FIG. 3, a flowchart that graphically illustrates amethod 300 of facilitating the search of a document database isprovided. Although the steps associated with the blocks of FIG. 3 willbe described as being separate tasks, in other embodiments, the blocksmay be combined or omitted. Further, while the steps associated with theblocks of FIG. 3 will described as being performed in a particularorder, in other embodiments, the steps may be performed in a differentorder.

Still referring to FIG. 3, at block 302, a graphical user interface isdisplayed. In some embodiments, the graphical user interface isdisplayed on a display device of the user computing device 12 a. Thegraphical user interface may be displayed in response to a messagereceived from the server computing device 12 b, including one or moreelements to be displayed in the graphical user interface. The servercomputing device 12 b may generate the message or information to bedisplayed with the display logic 48. The graphical user interfacesdescribed herein may facilitate the generation of search queries, theiterative refinement and manipulation of search queries, and theunderstanding of the estimated relevance of query results in anintuitive and user-friendly manner such that a user may identify desiredcontent and have confidence that the identified content is relevant. Thegraphical user interfaces described herein may be particularly usefulwhen the user computing device 12 a is a tablet device or smartphone.Furthermore, it should be understood that any of the graphical userinterfaces and elements described herein may be embedded or integratedin various product features and at various points of a search process.

For example, a graphical user interface displayed in accordance withsome embodiments is depicted in FIG. 4. It should be understood thatembodiments are not limited to the configurations of the graphical userinterfaces illustrated throughout the figures, and that other graphicaluser interface configurations are possible. In one embodiment, thecomputer network 10 is the Internet and the graphical user interfacesdescribed herein are presented to the user on a display device of theuser computing device 12 a via a web browser.

Referring to FIG. 4, the graphical user interface 400 includes a queryinput element 410, a query visualization and manipulation element 420,an estimated relevance element 430, and a results feedback element 440.The query input element 410 is configured to request a submission of asearch query from a user. In some embodiments, text input may beprovided in the query input element 410, such as when a user can selectthe query input element 410 as a field of entry and type text into thequery input element 410. In some embodiments, a user may have beenpresented a separate search screen (e.g., a user interface with a searchbox and a “search” button) used by the user to enter the search query,such as by typing the search query into the search box or providing thesearch query by speaking the search query into a microphone. It shouldbe understood that the user may interact with the user interfacesprovided herein via voice to provide the user with a natural interactionexperience, which may be useful in some embodiments in which the usercomputing device 12 a is a tablet or smartphone. In embodiments in whichthe search query is input via voice, the voice input may be translated,parsed, or processed in some manner by a speech recognition algorithm toproduce the search query. In some embodiments, the query input element410 may include one or more prompts or screens that may guide a userthrough various pieces of information used to construct the search query(e.g., prompting a user to enter a practice area, a date range, ajurisdiction, one or more search query terms, or the like).

Still referring to FIG. 4, the graphical user interface 400 includes aquery visualization and manipulation element 420. The queryvisualization and manipulation element 420 may provide a visualrepresentation of the search query that was input into the query inputelement 410 and may facilitate the editing of the search query. Thequery visualization and manipulation element 420 depicted in FIG. 4includes a plurality of nodes 422 a, 422 b, 422 c and a plurality ofconnectors 424 a, 424 b. The nodes have a different shape from theconnectors in the embodiment depicted in FIG. 4. However, the nodes andconnectors may be the same shape in other embodiments.

Each of the plurality of nodes 422 a, 422 b, 422 c corresponds to aquery term of the search query that was input into the query inputelement 410. For example, node 422 a graphically represents the “food”query term. Node 422 b graphically represents the “poisoning” queryterm. Node 422 c graphically represents the “negligence” query term. Insome embodiments, one or more of the plurality of nodes 422 a, 422 b,422 c may include a concept node, which may include one or more queryterms of the search query, one or more synonyms of one or more queryterms, one or more variants of one or more query terms, or anycombination of one or more query terms, one or more synonyms, and one ormore variants. For example, FIG. 5 below describes how one or moresynonyms and variants may be added to the node 422 b, which isrepresentative of the “poisoning” query term.

Each of the connectors 424 a, 424 b connects a pair of nodes and isrepresentative of a required proximity of the corresponding query termsof the connected pair of nodes. For example, connector 424 a connectsnode 422 a and node 422 b and represents a requirement that the “food”query term corresponding to node 422 a and the “poisoning” query termcorresponding to node 422 b be contained within the same document inorder for a document to be identified as matching the search query. Theconnectors may represent a document proximity (a requirement that thequery terms corresponding to a pair of nodes must be contained withinthe same document in order for a document to be identified as matchingthe search query), a paragraph proximity (a requirement that the queryterms corresponding to a pair of nodes must be contained within the sameparagraph in order for a document to be identified as matching thesearch query), a sentence proximity (a requirement that the query termscorresponding to a pair of nodes must be contained within the samesentence in order for a document to be identified as matching the searchquery), a phrase proximity (a requirement that the query termscorresponding to a pair of nodes must be contained within the samephrase in order for a document to be identified as matching the searchquery), or the like

Still referring to FIG. 4, the query visualization and manipulationelement 420 includes three query component drop boxes. Namely, the queryvisualization and manipulation element 420 includes a content type dropbox 426 a, a jurisdiction drop box 426 b, and a practice area drop box426 c. The content type drop box 426 a may allow a user to limit thesearch to documents of a particular content type, such as cases,statutes, articles, and the like. In the embodiment depicted in FIG. 4,the content type drop box 426 a displays “cases,” indicating that thesearch will be limited to cases. The content type drop box 426 a may beautomatically populated based on text input into the query input element410 (e.g., populating the content type drop box 426 a with “cases” basedon the presence of the term “cases” in the search query input into thequery input element 410). The content type drop box 426 a may also bemanipulated by a user, such as when a user clicks on the content typedrop box 426 a, a list of possible content types is displayed, and auser selects one of the displayed content types.

Still referring to FIG. 4, the jurisdiction drop box 426 b may allow auser to limit the search to documents from a particular jurisdiction,such as cases or other legal documents within a given state, court,circuit, or the like. In the embodiment depicted in FIG. 4, thejurisdiction drop box 426 b displays “California,” indicating that thesearch will be limited to content associated with California. Thejurisdiction drop box 426 b may be automatically populated based on textinput into the query input element 410 (e.g., populating thejurisdiction drop box 426 b with “California” based on the presence ofthe term “California” in the search query input into the query inputelement 410). The jurisdiction drop box 426 b may also be manipulated bya user, such as when a user clicks on the jurisdiction drop box 426 b, alist of possible jurisdictions is displayed, and a user selects one ofthe displayed jurisdictions.

Still referring to FIG. 4, the practice area drop box 426 c may allow auser to limit the search to documents associated with a particularpractice groups, such as document associated with torts, intellectualproperty, real property, criminal law, constitutional law, or the like.The practice area drop box 426 c may be automatically populated based ontext input into the query input element 410 and/or may be populated byuser manipulation, such as when a user clicks on the practice area dropbox 426 c, a list of possible practice areas is displayed, and a userselects one of the displayed practice areas. It should be understoodthat other embodiments may include more or fewer query component dropboxes than depicted in FIG. 4, such that other query components may beadded or removed by manipulation with a drop box or other user prompt.

Still referring to FIG. 4, the query visualization and manipulationelement 420 includes a related terms element 428. The related termselement 428 displays one or more terms related to one or more of thesearch terms in the search query. The user may add one or more of therelated terms to the query by adding the term to the query input box,dragging the term to create a node next to the nodes and connectors ofthe query, or the like. In some embodiments, the related terms aregenerated by the server computing device 12 b based on one or more queryterms of the search query transmitted from the user computing device 12a to the server computing device 12 b. In such embodiments, the servercomputing device 12 b transmits the related terms to the user computingdevice 12 a for display on the user computing device 12 a.

Still referring to FIG. 4, the graphical user interface 400 includes anestimated relevance element 430. In the embodiment depicted in FIG. 4,the estimated relevance element 430 includes an estimated relevanceindicator 432. The estimated relevance indicator 432 provides a visualindication of the degree to which a result set of documents (identifiedby searching a document database with a search query) are relevant tothe search query. In the embodiment depicted in FIG. 4, the estimatedrelevance indicator 432 is a bar. The bar may be displayed to provide avisual indication of a degree to which the result set of document arerelevant to the search query, such as by varying the length of the barsuch that the length of the bar is indicative of the degree to which theresult set of documents are relevant to the search query. In someembodiments, a color of the estimated relevance indicator 432 isindicative of the degree to which the result set of documents arerelevant to the search query, such as when the estimated relevanceindicator 432 is red to indicate a low level of relevance, the estimatedrelevance indicator 432 is yellow to indicate a moderate level ofrelevance, and the estimated relevance indicator 432 is green toindicate a high level of relevance. The estimated relevance element 430may also include a low relevance region 434 a, a moderate relevanceregion 434 b, and a high relevance region 434 c. When the estimatedrelevance indicator 432 extends into the low relevance region 434 a, theresult set is estimated to be of low relevance. When the estimatedrelevance indicator 432 extends into the moderate relevance region 434b, the result set is estimated to be of moderate relevance. When theestimated relevance indicator 432 extends into the high relevance region434 c, the result set is estimated to be of high relevance. In otherembodiment, the estimated relevance indicator 432 may be different fromthe embodiment depicted in FIG. 4 and described herein, such as inembodiment in which the estimated relevance indicator 432 is a gauge ora textual indication of the estimated relevance.

Still referring to FIG. 4, the graphical user interface 400 includes aresults feedback element 440. The results feedback element 440 maydisplay a preview or other display of a result set of documentsidentified by searching the document database with the search query. Inone embodiment, the results feedback element 440 provides preview orother display of a subset of the result set of documents. In anotherembodiment, all of the returned electronic documents are provided inresults feedback element 440. A scroll bar may be provided to enable theuser to scroll through the returned electronic documents. The visualindication of the displayed documents may take on a variety of forms. Insome embodiments, such as the embodiment depicted in FIG. 4, a documentidentifier and a relevant portion of the text within the document may bedisplayed. Other visual representations are also possible. The graphicaluser interface 400 also includes a number of results indicator 442 thatis indicative of a number of documents in the result set of documents.In some embodiments, the results may be viewed in further detail by usermanipulation of the view results button 444. The color of the viewresults button may provide a visual indication of the estimatedrelevance of the result set of document, such as when the view resultsbutton 444 is red to indicate a low level of relevance, the view resultsbutton 444 is yellow to indicate a moderate level of relevance, and theview results button 444 is green to indicate a high level of relevance

Referring once again to FIG. 3 (and FIG. 1), at block 304, a searchquery may be received. In some embodiments, the search query received atblock 304 may have been entered into the query input element 410 of thegraphical user interface 400 displayed by the user computing device 12a, transmitted from the user computing device 12 a to the servercomputing device 12 b in response to initiation of the search by a user(e.g., by pressing enter or selecting a search initiation icon), andreceived by the server computing device 12 b. The received search queryincludes a plurality of terms. For example, referring to FIGS. 4 and 1,a search query of “California food poisoning negligence cases” may beentered into the query input element 410 of the graphical user interface400 displayed by the user computing device 12 a, transmitted from theuser computing device 12 a to the server computing device 12 b, andreceived by the server computing device 12 b.

Referring once again to FIGS. 3 and 2, at block 306, the servercomputing device 12 b may use the search logic 44 to search the documentdatabase 38 a to identify a result set of document that are identifiedbased on the search query received at block 304. The search logic 44 mayemploy a search algorithm that identifies the result set of documents asthe documents in the document database 38 a that include one or more ofthe query terms of the search query and satisfy any additionalconstraints associated with the search query, such as content type,proximity of terms, or the like. A variety of search algorithms may beemployed by the search logic 44.

Still referring to FIGS. 3 and 2, at block 308, the server computingdevice 12 b may use the estimated relevance score calculation logic 46to calculate an estimated relevance score for the result set ofdocuments. As noted above, the estimated relevance score is indicativeof a degree to which the result set of documents are relevant to thesearch query. In some embodiments, the estimated relevance score may becalculated based on one or more context characteristics, one or morequery characteristics, or one or more results characteristics. Theestimated relevance score may be calculated based on a plurality ofweighted score components, which may include one or more contextcharacteristics, one or more query characteristics, or one or moreresults characteristics, as will be described in the following sectionsthat describe the various context characteristics, querycharacteristics, and results characteristics and how the estimatedrelevance score may be calculated based on them.

Calculation of the Estimated Relevance Score Based on ContextCharacteristics

When the estimated relevance score is calculated based on one or morecontext characteristics, the server computing device 12 b may determineone or more context characteristics that are indicative of a context ofthe search query and then calculate the estimated relevance score basedon the one or more context characteristics.

The one or more context characteristics may include a user profile,which may include such information as a user identifier, a practicearea, a jurisdiction, user preferences, or the like. In someembodiments, the estimated relevance score is calculated based on theuser profile. For example, the estimated relevance score may becalculated as higher based on the user profile (e.g., by calculating theestimated relevance score as higher when a high proportion of the resultset of documents are from the practice area included in the userprofile) or may be calculated as lower based on the user profile (e.g.,by calculating the estimated relevance score as lower when a lowerproportion of the result set of documents are from the practice areaincluded in the user profile). In some embodiments, the estimatedrelevance score may be calculated based on a plurality of weighted scorecomponents, including a user profile component that is calculated basedon the user profile. In some embodiments, the estimated relevance scoremay be calculated as another function of the user profile.

The one or more context characteristics may include a device type, whichmay indicate whether the user computing device 12 a is a personalcomputer, a laptop computer, a tablet, a smartphone, or the like. Insome embodiments, the estimated relevance score is calculated based onthe device type, such as when the estimated relevance score is onlycalculated or utilized when the device type is a tablet or smartphone.

The one or more context characteristics may include a geographiclocation or a network location. In some embodiments, the estimatedrelevance score is calculated based on the geographic location ornetwork location. For example, the estimated relevance score may becalculated based on a geographic location indicative of a user beingaway from an office or a geographic location indicative of a user beingin an office. In some embodiments, the estimated relevance score iscalculated based on the geographic location or network location, such aswhen the estimated relevance score is only calculated or utilized whenthe user is away from the office.

Calculation of the Estimated Relevance Score Based on QueryCharacteristics

When the estimated relevance score is calculated based on one or morequery characteristics, the server computing device 12 b may determineone or more query characteristics that are indicative of acharacteristic of the search query and then calculate the estimatedrelevance score based on the one or more query characteristics.

The one or more query characteristics may include a number of queryterms in the search query. In some embodiments, the estimated relevancescore is calculated based on the number of query terms. For example, theestimated relevance score may be calculated as proportional to thenumber of query terms. In some embodiments, the estimated relevancescore may be calculated as a bell-shaped function of the number of queryterms, such that the estimated relevance score is lower when there are alow number of query terms (e.g., 3 or less query terms), higher whenthere are a medium number of query terms (e.g., 4 to 7 query terms), andlower when there are a high number of query terms (e.g., 8 or more queryterms). In some embodiments, the estimated relevance score may becalculated based on a plurality of weighted score components, includinga number of query terms component. In some embodiments, the estimatedrelevance score may be calculated as another function of the number ofquery terms.

The one or more query characteristics may include an inclusion of arecognized phrase. In some embodiments, the estimated relevance score iscalculated based on the inclusion of a recognized phrase. For example,the estimated relevance score may be calculated as higher when thesearch query includes a recognized phrase, such as the recognized legalphrase “adverse possession,” “eminent domain,” “fiduciary duty,” or thelike. The estimated relevance score may be calculated as lower when thesearch query does not include a recognized phrase, such as when thesearch query includes disjoint query terms that are not part of one ormore recognized phrases. In some embodiments, one or more recognizedphrases may be stored in the data storage component 36 or the memorycomponent 40 of the server computing device 12 b. In some suchembodiments, the estimated relevance score is calculated based onwhether the search query includes at least one of the recognized phrasesstored in the data storage component 36 or the memory component 40. Insome embodiments, the estimated relevance score may be calculated basedon a plurality of weighted score components, including an inclusion of arecognized phrase component. In some embodiments, the estimatedrelevance score may be calculated as another function of the inclusionof a recognized phrase.

The one or more query characteristics may include an inclusion of acitation. In some embodiments, the estimated relevance score iscalculated based on the inclusion of a citation. For example, theestimated relevance score may be calculated as higher when the searchquery includes a citation. The estimated relevance score may becalculated as lower when the search query does not include a citation.In the context of a legal search query used to search legal documentscontained in the document database 38 a of the server computing device12 b, the estimated relevance score may be calculated as higher when thesearch query includes a legal citation, such as a citation to a courtcase, a citation to a statute, or the like. Conversely, the estimatedrelevance score may be calculated as lower when the search query doesnot include a legal citation. In some embodiments, the estimatedrelevance score may be calculated based on a plurality of weighted scorecomponents, including the inclusion of a citation component. In someembodiments, the estimated relevance score may be calculated as anotherfunction of the inclusion of a citation.

The one or more query characteristics may include an inclusion of asearch filter. In some embodiments, the estimated relevance score iscalculated based on the inclusion of the search filter. For example, theestimated relevance score may be calculated as higher when the searchquery includes a search filter. The estimated relevance score may becalculated as lower when the search query does not include a searchfilter. The search filter may be a date range filter, such as when auser limits the search to a particular date range by entering a daterange in the query input element 410 or selects a date range from a dropdown box or other filter. The search filter may be a jurisdictionfilter, such as when a user limits the search to cases or statutes froma particular jurisdiction by entering a jurisdiction in the query inputelement 410 or selecting a jurisdiction from the jurisdiction drop box426 b. The search filter may be a content type filter, such as when auser limits the search to a particular content type (e.g., cases,statutes, law review articles, or the like) by entering a content typein the query input element 410 or selecting a content type from thecontent type drop box 426 a. The search filter may be a practice areafilter, such as when a user limits the search to documents from aparticular practice area (e.g., torts, intellectual property, realproperty, criminal law, constitutional law, or the like) by entering apractice area in the query input element 410 or selecting a practicearea from the practice area drop box 426 c. It should be understood thatthe estimated relevance score may be calculated based on a variety ofadditional filters that may be applied to the search query other thanthe filters specifically described herein.

The one or more query characteristics may include an ambiguity score. Insome embodiments, the estimated relevance score is calculated based onthe ambiguity score. The ambiguity score provides a measurement of howambiguous a query is based on its linguistics. In some embodiments, theserver computing device 12 b uses query ambiguity determination logicstored in the memory component 40 to analyze the ambiguity of the queryand to calculate the ambiguity score. In some embodiments, the estimatedrelevance score may be calculated based on a plurality of weighted scorecomponents, including an ambiguity score component. In some embodiments,the estimated relevance score may be calculated as another function ofthe ambiguity score.

The one or more query characteristics may include a degree of querysimilarity. The degree of query similarity is indicative of a degree ofsimilarity to at least one previous search query. For example, thedegree of query similarity may be a degree of similarity to a previoussearch query by another user in a similar context (e.g., in the samecontent area, in the same practice area, or the like). The degree ofquery similarity may be a degree of similarity to a previous searchquery that identified a result set from which information was captured,as evidenced by a user downloading, printing, e-mailing, or savingdocuments from the previously identified result set or content from thedocuments of the previously identified result set. The degree of querysimilarity may be a degree of similarity to a previous search query thatidentified a result set from which documents were analyzed, as evidencedby a user performing a citation analysis, highlighting, annotating, orviewing documents from the previously identified result set or contentfrom the documents of the previously identified result set. Theestimated relevance score may be calculated based on the degree of querysimilarity. For example, the estimated relevance score may be calculatedas higher when the search query is similar to a previous search querythat included a result set from which documents or content of thedocuments of the result set was downloaded, printed, e-mailed, saved, orthe like. In some embodiments, the estimated relevance score may becalculated based on a plurality of weighted score components, includinga degree of query similarity component. In some embodiments, theestimated relevance score may be calculated as another function of thedegree of query similarity.

Calculation of the Estimated Relevance Score Based on ResultsCharacteristics

When the estimated relevance score is calculated based on one or moreresults characteristics, the server computing device 12 b may determineone or more results characteristics that are indicative of the resultset of documents and then calculate the estimated relevance score basedon the one or more results characteristics.

The one or more results characteristics may include a terms relevance.The terms relevance may be indicative of an extent to which the queryterms of the search query match one or more terms in at least onedocument of the result set of documents. The terms relevance may becalculated by calculating a term frequency-inverse document frequency(“tf-idf”) for each of the query terms in the search query in each ofthe documents in the result set of documents and then using thecalculated tf-idf values to determine the estimated relevance score. Theestimated relevance score may then be calculated based on the termsrelevance calculations for one or more of the documents in the resultset. The estimated relevance score is calculated based on the termsrelevance in a variety of ways, such as when the estimated relevancescore is calculated based on an average relevance per document of theresult set, based on an average relevance per document in a subset ofthe result set of documents (e.g., the top 25 results), a relevancedifference between a first document of the result set of documents and asecond document in the result set of documents (e.g., a tf-idfdifference between the first document of the top 25 results and thetwenty-fifty document of the top 25 results). In some embodiments, theestimated relevance score may be calculated based on a plurality ofweighted score components, including a terms relevance component. Insome embodiments, the estimated relevance score may be calculated asanother function of the terms relevance.

The one or more results characteristics may include a terms relevanceprecipitation. The terms relevance precipitation may be indicative of adifference in relevance between a first document of the result set ofdocuments and a second document of the result set of documents. In someembodiments, the terms relevance of the first document and the seconddocument may be calculated in any of the ways described in the precedingparagraph. In some embodiments, the estimated relevance score iscalculated based on the terms relevance precipitation. For example, asubset of relevant documents may be identified from the result set, suchas when the “top 25” documents by tf-idf score are identified. Thesubset of documents may be ranked from highest terms relevance to lowestterms relevance. The terms relevance precipitation may be calculated inthis example by subtracting the terms relevance of the lowest rankeddocument of the “top 25” documents from the terms relevance of thehighest ranked document of the “top 25” documents. The estimatedrelevance score may be calculated as proportional to the terms relevanceprecipitation because a high difference in relevance between the highestranked document in a subset and the lowest ranked document in the subsetis indicative of an effective differentiation in the relevance of thedocuments of the subset and may be indicative of an effective searchquery. In some embodiments, the estimated relevance score may becalculated based on a plurality of weighted score components, includinga terms relevance precipitation component. In some embodiments, theestimated relevance score may be calculated as another function of theterms relevance precipitation.

The one or more results characteristics may include a best paragraphsterms prevalence. The best paragraph terms prevalence may be indicativeof a number of query terms and/or synonyms of the query terms includedin at least one paragraph of at least one document of the result set ofdocuments. For example, one or more paragraphs in a document in theresult set may be identified as a “best paragraph” based on the presenceof query terms and/or synonyms of query terms in the paragraph. Thenumber of the query terms and/or synonyms of the query terms in the“best paragraph” may be calculated. The best paragraphs terms prevalencefor an identified “best paragraph” may be calculated in a number ofways, including based on the absolute number of query terms in theidentified “best paragraph,” the absolute number of query terms andsynonyms of the query terms in the identified “best paragraph,” theratio of query terms in the identified “best paragraph” to the totalnumber of terms in the identified “best paragraph,” the ratio of queryterms and synonyms of the query terms in the “best paragraph” to thetotal number of terms in the identified “best paragraph,” and the like.The estimated relevance score may be calculated based on the bestparagraphs terms prevalence by using the best paragraphs termsprevalence of a single “best paragraph” from each document in the resultset, by using the best paragraphs terms prevalence of multiple “bestparagraphs” from each document in the result set, by using the bestparagraphs terms prevalence of a single “best paragraph” from a subsetof the documents in the result set, or by using the best paragraphsterms prevalence of multiple documents from a subset of the documents inthe result set. In some embodiments, the estimated relevance score maybe calculated based on a plurality of weighted score components,including a best paragraphs terms prevalence component. In someembodiments, the estimated relevance score may be calculated as anotherfunction of the best paragraphs terms prevalence.

The one or more results characteristics may include a topical diversity.The topical diversity may be indicative of a variance in topics withinthe result set of documents. The topical diversity may be calculated ina number of ways. For example, in the context of legal documents, thetopical diversity may be calculated based on a degree that query termsmatch terms in a legal taxonomy (e.g., by comparing the query terms toterms included in a separate legal taxonomy stored in the data storagecomponent 36 or the memory component 40 of the server computing device12 b) or based on practice areas associated with documents in the resultset (e.g., by comparing a practice area associated with the documents inthe result set with a practice area of the search query). The topicaldiversity may be calculated for the entire result set of documents orfor a subset of the documents in the result set of documents. Theestimated relevance score may be calculated based on the topicaldiversity. For example, the estimated relevance score may be calculatedas inversely proportional to the topical diversity because a result setthat is focused on a particular topic is more likely to be relevant tothe search query than a result set that includes documents scatteredamong a number of topics. In some embodiments, the estimated relevancescore may be calculated based on a plurality of weighted scorecomponents, including a topical diversity component. In someembodiments, the estimated relevance score may be calculated as anotherfunction of the topical diversity.

The one or more results characteristics may include a contentvariability. The content variability may be indicative of a variance incontent within the result set of documents. The content variability mayinclude a variance in recency among the result set of documents, avariance in jurisdiction (in the case of legal documents) among theresult set of documents, a variance in invention class (in the case ofpatent documents) among the result set of documents, a variance in legalissues among the result set of documents, or the like. The contentvariability may be calculated for the entire result set of documents orfor a subset of the documents in the result set of documents. Theestimated relevance score may be calculated based on the contentvariability. For example, the estimated relevance score may becalculated as inversely proportional to the content variability becausea result set that is focused on a particular content aspect is morelikely to be relevant to the search query than a result set thatincludes documents scattered among a number of content aspects. In someembodiments, the estimated relevance score may be calculated based on aplurality of weighted score components, including a content variabilitycomponent. In some embodiments, the estimated relevance score may becalculated as another function of the content variability.

The one or more results characteristics may include a terms proximity.The terms proximity may be indicative of a proximity of the query termswithin a portion of at least one document of the result set ofdocuments. The proximity of the query terms within a portion of adocument indicates the degree to which the terms are concentrated withinthe document versus distributed throughout the document. A document witha higher terms proximity is likely to be more relevant because it has agreater proportion of query terms proximate to one another. The termsproximity may be calculated in a number of ways, such as by determiningan average number of words between query terms, by determining anaverage number of words between query terms in a paragraph of thedocument, or the like. The terms proximity may also be calculated basedon the proximity between both query terms and synonyms to the queryterms. The estimated relevance score may then be calculated based on theterms proximity for one or more of the documents in the result set. Insome embodiments, the estimated relevance score may be calculated basedon a plurality of weighted score components, including a terms proximitycomponent. In some embodiments, the estimated relevance score may becalculated as another function of the terms proximity.

The one or more results characteristics may include a query to coreterms ratio. The query to core terms ratio may be indicative of a degreeof overlap between query terms and core terms in at least one documentof the result set of documents. Each of the documents in the result setmay include one or more terms identified as “core terms” for thedocument, which may be indicative of terms that summarize the content orfocus of the document or terms that indicate important concepts withinthe document. The query to core terms ratio may be calculated bydividing the number of query terms with the number of core terms in thedocument that are also query terms. A document with a lower query tocore terms ratio is likely to be more relevant to a given search query.In some embodiments, the query to core terms ratio may be calculated bydividing the number of query terms with the number of core terms in thedocument that are also query terms or synonyms of the query terms. Theestimated relevance score may then be calculated based on the query tocore terms ratio for one or more of the documents in the result set. Insome embodiments, the estimated relevance score may be calculated basedon a plurality of weighted score components, including a query to coreterms ratio component. In some embodiments, the estimated relevancescore may be calculated as another function of the query to core termsratio.

The one or more results characteristics may include a document recencyindicator. The document recency indicator may be indicative of a date ofat least one document of the result set of documents. In some contexts,such as the news or financial context, a document that is more recent islikely to be more relevant. The estimated relevance score may then becalculated based on the document recency indicator for one or more ofthe documents in the result set or for the result set as a whole. Insome embodiments, the estimated relevance score may be calculated basedon a plurality of weighted score components, including a documentrecency indicator. In some embodiments, the estimated relevance scoremay be calculated as another function of the document recency indicator.

The one or more query characteristics may include a number of documentsin the result set of documents. In some embodiments, the estimatedrelevance score is calculated based on the number of documents in theresult set of documents. For example, the estimated relevance score maybe calculated as inversely proportional to the number of documents inthe result set of documents. In some embodiments, the estimatedrelevance score may be calculated as a bell-shaped function of thenumber of documents in the result set of documents, such that theestimated relevance score is lower when there are a low number ofdocuments in the result set of documents, higher when there are a mediumnumber of documents in the result set of documents, and lower when thereare a high number of documents in the result set of documents. In someembodiments, the estimated relevance score may be calculated based on aplurality of weighted score components, including a number of documentsin the result set of documents. In some embodiments, the estimatedrelevance score may be calculated as another function of the number ofdocuments in the result set of documents.

Examples of Weighted Estimated Relevance Score Components

As noted above, the estimated relevance score may be calculated based ona plurality of weighted score components, which may include one or moreof the context characteristics, one or more of the querycharacteristics, or one or more of the results characteristics describedabove. The estimated relevance score may have a value between 0% and100% or 0 and 1. Of course, it should be understood that the estimatedrelevance score may vary between other lower bounds and upper bounds inother embodiments. In some embodiments, a weight is assigned to each ofa plurality of weighted components used to calculate the estimatedrelevance score. In some embodiments in which the estimated relevancescore varies between 0% and 100%, the weights of each of the weightedcomponents add up to 100%. The particular components used to calculatethe estimated relevance score and the weights of the components may varybased on the use case or the context, among other variables. A fewnon-limiting examples will now be provided of score components and theirassociated weights.

In a limited computing context, such as when a user utilizes a tablet orsmartphone to perform a query, the following characteristics and weightsmay be used to calculate the estimated relevance score. In the limitedcomputing context, the results characteristics may be determined for theentire result set or a subset of the result set, such as the top 25documents.

TABLE 1 Type Component Weight (%) Context User profile 5 Query Number ofquery terms 15 Inclusion of a filter 20 Ambiguity score 10 ResultsNumber of documents in result set 15 Terms relevance 20 Terms relevanceprecipitation 10 Document recency indicator 5

In a more robust computing context, such as when a user utilizes adesktop or laptop computer, the following characteristics and weightsmay be used to calculate the estimated relevance score. In the morerobust computing context, the results characteristics may be determinedfor the entire result set or a subset of the result set, such as the top50 documents or the top 100 documents

TABLE 2 Type Component Weight (%) Context User profile 5 Query Number ofquery terms 5 Inclusion of a filter 5 Ambiguity score 5 Degree ofsimilarity to previous query 5 Results Number of documents in result set5 Terms relevance 15 Terms relevance precipitation 10 Best paragraphsterms prevalence 10 Topical diversity 15 Terms proximity 5 Query to coreterms ratio 10 Document recency indicator 5

In some embodiments, it may be desirable to utilize only one type ofcharacteristics to calculate the estimated relevance score. For example,in some embodiments, only results characteristics are utilized tocalculate the estimated relevance score, such as shown in thenon-limiting example below.

TABLE 3 Type Component Weight (%) Results Number of documents in resultset 10 Terms relevance 15 Terms relevance precipitation 10 Bestparagraphs terms prevalence 15 Topical diversity 15 Terms proximity 10Query to core terms ratio 15 Document recency indicator 10

The particular components used to calculate the estimated relevancescore and the weights of the components may vary based on the type ofquery. For example, the components and weights shown two charts abovemay be used for a case law search while the chart below may be used fora search for patent documents. The case law search uses a topicaldiversity component while the patent search uses a class/subclassdiversity because topical diversity is more indicative of documentrelevance in the case law context while class/subclass diversity is moreindicative of document relevance in the patent document context. Theestimated relevance score for the patent document search does notinclude ambiguity score or document recency indicator because thesecharacteristics may be useful in the case law context, but not useful inthe patent document context.

TABLE 4 Type Component Weight (%) Context User profile 5 Query Number ofquery terms 5 Inclusion of a filter 5 Degree of similarity to previousquery 5 Results Number of documents in result set 10 Terms relevance 20Terms relevance precipitation 10 Best paragraphs terms prevalence 10Class/subclass diversity 15 Terms proximity 5 Query to core terms ratio10

Accordingly, it should be understood that the particular components andthe weights of the components that are used to calculate the estimatedrelevance score may vary based on a number of factors, such as thedevice used to perform the search, the type of search, the userperforming the search, or the like.

Referring once again to FIG. 3 (and FIGS. 2 and 4), at block 310, theserver computing device 12 b may use the display logic 48 to update thegraphical user interface 400. The results feedback element 440 may beupdated based on the result set of documents identified at block 306. Inthe embodiment depicted in FIG. 4, the results feedback element 440 isupdated to display relevant portions of the top five ranked documents ofthe result set of documents. The number of results indicator 442 is alsoupdated to display “134,” indicating that there are 134 documents in theresult set of documents. The estimated relevance element 430 is updatedto display an estimated relevance indicator 432 to provide a visualindication of the degree to which the result set of documents arerelevant to the search query. The estimated relevance indicator 432depicted in FIG. 4 indicates that the result set of documents identifiedby the initial search query are estimated to be of moderate relevance,as indicated by the length of the bar extending into the moderaterelevance region 434 b. The estimated relevance indicator may provide avisual indication to a user of the user computing device 12 a that queryimprovement is suggested, such as when the estimated relevance indicator432 only extends into the low relevance region 434 a or into themoderate relevance region 434 b. The view results button 444 may also bedisplayed as yellow to indicate that the results are estimated to be ofmoderate relevance.

Still referring to FIG. 3 (and FIGS. 2 and 4), at block 312, the servercomputing device 12 b may determine whether the estimated relevancescore is less than a relevance score threshold. The relevance scorethreshold may depend on the context of the search. In some embodiments,the relevance score threshold may be 33%, 50%, or 66%. If the estimatedrelevance score is determined to be greater than or equal to therelevance threshold, the server computing device 12 b may not provide asuggestion for improving the query and the method 300 may end at block314. After the method 300 ends, the user may still be able to review theresults in the results feedback element 440, create a new query, ormanipulate an existing query.

Still referring to FIG. 3 (and FIG. 2), in response to determining thatthe estimated relevance score is less than the relevance scorethreshold, the query visualization and manipulation element 420 may beupdated to provide at least one suggestion for improving the searchquery at block 314. Referring now to FIG. 5, a synonyms and variants box510 may be displayed as a suggestion for improving the search query. Thesynonyms and variants box 510 includes synonyms, variants, or relatedterms to the “poisoning” search term. Specifically, the synonyms andvariants box 510 includes “poison!” (which includes all stemmed variantsof the term “poison,” e.g., poison, poisoned, poisoning, etc.),“botulism,” “salmonella,” “toxic,” “tainted,” and “unsafe.” In someembodiments, the synonyms and variants box 510 is displayedautomatically in response to determining that the estimated relevancescore is less than the relevance score threshold. In other embodiments,the synonyms and variants box 510 is displayed in response to a usermanipulating or selecting node 422 b (the node corresponding to the“poisoning” search term). In some embodiments, the suggestions forimproving the search query may include displaying related terms to oneor more of the query terms (such as shown in the related terms element528 of FIG. 5), suggesting one or more query terms, or suggesting one ormore search filters. In some embodiments, the suggestion for improvingthe search query may include providing for display a prompt requestingan entry of at least one predefined query component or filter, such as ajurisdiction, a date, a practice area, a content type, or the like. Itshould be understood that other query suggestions are possible. The usermay select a number of terms from the synonyms and variants box 510,which are added to the concept node 422 b, as depicted in FIG. 5 wherethe user has selected “poison!,” “salmonella,” “toxic,” and “tainted.”

Still referring to FIG. 3 (and FIG. 2), in response to providing atleast one suggestion for improving the search query (as described abovewith respect to block 314), a query improvement may be received and animproved search query may be generated at block 316 in response to thereceived query improvement. For example, referring once again to FIG. 5,the query improvement of adding the selected “poison!,” “salmonella,”“toxic,” and “tainted” terms to the query may be received by the servercomputing device 12 b. The server computing device 12 b may generate animproved search query based on the received query improvement by addingthe selected “poison!,” “salmonella,” “toxic,” and “tainted” terms.After generating the improved search query, the server computing device12 b may return to block 306 to search the document database 38 a usingthe improved search query to identify an improved result set ofdocuments. The server computing device 12 b may calculate an improvedestimated relevance score for the improved result set of documents inthe manner as described above with respect to block 308. The servercomputing device 12 b may update the graphical user interface 500 byupdating the results feedback element based on the improved result setof documents and update the estimated relevance indicator 432 for theimproved result set of documents, as described above with respect toblock 310. As shown in FIG. 5, the improved estimated relevance scorefor the result set identified based on the improved search query ishigher, as shown by the updated estimated relevance indicator 432advancing farther to the right.

The user may continue to manipulate and improve the query in the queryvisualization and manipulation element 420 and/or based on suggestedquery improvements until the user is satisfied with the estimatedrelevance of the result set, as shown by the estimated relevanceindicator in the graphical user interface. For example, the user may addthe related term “liability” to the search query. The “liability” termmay be added to the query by allowing a user to drag and drop a nodecorresponding to the “liability” term from the related terms element 528(as shown in FIG. 5) to be proximate to the other nodes of the query, asshown in FIG. 6, which graphically depicts the “liability” term added tothe search query as node 620. After adding the liability term to thesearch query, the server computing device 12 b may generate an improvedsearch query, search the database to identify an improved result set ofdocuments, calculate an improved estimated relevance score for theimproved result set of documents, and update the graphical userinterface by updating the results feedback element based on the improvedresult set of documents, as described above with respect to blocks306-310. As shown in FIG. 6, the improved estimated relevance score forthe result set identified based on the improved search query is higher,as shown by the updated estimated relevance indicator 432 advancingfarther to the right and into the high estimated relevance region.

Referring now to FIG. 7, the user may further improve the query byselecting “torts” as the practice area from the practice area drop box426 c. In some embodiments, a prompt is displayed requesting an entry ofthe practice area, or another predefined query component, such asjurisdiction, date, and content type. After adding the setting torts asthe practice area, the server computing device 12 b may generate animproved search query, search the database to identify an improvedresult set of documents, calculate an improved estimated relevance scorefor the improved result set of documents, and update the graphical userinterface by updating the results feedback element based on the improvedresult set of documents, as described above with respect to blocks306-310. As shown in FIG. 7, the improved estimated relevance score forthe result set identified based on the improved search query is higher,as shown by the updated estimated relevance indicator 432 advancingfarther to the right into the high estimated relevance region.

Referring now to FIG. 8, as a final improvement to the search query, theuser may change the connectors 824 a, 824 b, and 824 c, such that onlydocuments with: (i) “food” within the same paragraph as “poisoning” (andits synonyms and variants); (ii) “poisoning” (and its synonyms andvariants) within the same paragraph as “negligence”; and (iii)“negligence” within the same page as “liability” will be included in theresult set of documents. The connectors may be changed by clicking onthe connector to cycle among various proximity settings, such assentence, paragraph, page, document, and the like. After changing theconnectors as shown in FIG. 8, the server computing device 12 b maygenerate an improved search query, search the database to identify animproved result set of documents, calculate an improved estimatedrelevance score for the improved result set of documents, and update thegraphical user interface by updating the results feedback element basedon the improved result set of documents, as described above with respectto blocks 306-310. As shown in FIG. 8, the improved estimated relevancescore for the result set identified based on the improved search queryis still higher, as shown by the updated estimated relevance indicator432 advancing farther to the right into the high estimated relevanceregion. The number of results has been narrowed to 17, indicating thatthe documents of the result set are highly relevant to the search queryand much fewer than the initial set of results identified by the initialsearch query of FIG. 4. A user may now proceed with inspecting thedocuments in the result set with confidence that relevant results havebeen identified and that wasted time will be reduced.

Accordingly, it should be understood that embodiments described hereindisplay estimated relevance indicators for result sets of documentsbased on calculated estimated relevance scores. The displayed estimatedrelevance indicator provides a useful visual indication of the degree towhich the result set of documents are relevant to the search query.Providing a visual indication of the degree to which a result set ofdocument is relevant to a search query may allow a user to formulateimproved queries, inspect more relevant documents, and save time byreducing the amount of time spent inspecting irrelevant documents.Furthermore, the graphical user interfaces described herein mayfacilitate the generation of search queries, the iterative refinementand manipulation of search queries, and the understanding of theestimated relevance of query results in an intuitive and user-friendlymanner such that a user may identify desired content and have confidencethat the identified content is relevant.

While particular embodiments have been illustrated and described herein,it should be understood that various other changes and modifications maybe made without departing from the spirit and scope of the claimedsubject matter. Moreover, although various aspects of the claimedsubject matter have been described herein, such aspects need not beutilized in combination. It is therefore intended that the appendedclaims cover all such changes and modifications that are within thescope of the claimed subject matter.

What is claimed is:
 1. A method for providing for display an estimatedrelevance indicator for a result set of documents, the methodcomprising: receiving, at a computer comprising a processor and a memorycomponent, a search query, wherein the search query includes a pluralityof query terms; searching a database using the search query to identifythe result set of documents, wherein the result set of documents areidentified based on the search query; calculating an estimated relevancescore for the result set of documents, wherein the estimated relevancescore is indicative of a degree to which the result set of documents arerelevant to the search query; providing for display on a graphical userinterface a results feedback element and an estimated relevance elementcomprising the estimated relevance indicator based on the estimatedrelevance score, wherein the estimated relevance indicator provides avisual indication of the degree to which the result set of documents arerelevant to the search query; updating the results feedback elementbased on the result set of documents; and updating the estimatedrelevance element to display the estimated relevance indicator based onthe estimated relevance score.
 2. The method of claim 1, furthercomprising determining one or more context characteristics indicative ofa context of the search query, wherein the estimated relevance score iscalculated based on the one or more context characteristics.
 3. Themethod of claim 2, wherein the one or more context characteristicsincludes a user profile or a device type.
 4. The method of claim 1,further comprising determining one or more query characteristicsindicative of a characteristic of the search query, wherein theestimated relevance score is calculated based on the one or more querycharacteristics.
 5. The method of claim 4, wherein the one or more querycharacteristics includes a number of the query terms in the searchquery, an inclusion of a recognized phrase, an inclusion of a citation,an inclusion of a filter, a degree of query similarity indicative of adegree of similarity to at least one previous search query.
 6. Themethod of claim 1, further comprising determining one or more resultscharacteristics indicative of a characteristic of the result set ofdocuments, wherein the estimated relevance score is calculated based onthe one or more results characteristics.
 7. The method of claim 6,wherein the one or more results characteristics includes: a termsrelevance indicative of an extent to which the query terms match one ormore terms in at least one document of the result set of documents, aterms relevance precipitation indicative of a difference in relevancebetween a first document of the result set of documents and a seconddocument of the result set of documents, a best paragraphs termsprevalence indicative of a number of the query terms and synonyms of thequery terms included in at least one paragraph of at least one documentof the result set of documents, a topical diversity indicative of avariance in topics within the result set of documents, a terms proximityindicative of a proximity of the query terms within a portion of atleast one document of the result set of documents, a query to core termsratio indicative of a degree of overlap between the query terms and coreterms in at least one document of the result set of documents, adocument recency indicator indicative of a date of at least one documentof the result set of documents, or a number of documents in the resultset of documents.
 8. The method of claim 1, wherein the estimatedrelevance score is calculated based on a plurality of weighted scorecomponents.
 9. The method of claim 1, further comprising providing atleast one suggestion for improving the search query when the estimatedrelevance score is less than a relevance score threshold.
 10. A methodfor providing for display an estimated relevance indicator for a resultset of documents, the method comprising: receiving, at a computercomprising a processor and a memory component, the search query, whereinthe search query includes a plurality of query terms; searching adatabase using the search query to identify a result set of documents,wherein the result set of documents are identified based on the searchquery; calculating an estimated relevance score for the result set ofdocuments, wherein the estimated relevance score is indicative of adegree to which the result set of documents are relevant to the searchquery; providing for display on a graphical user interface a resultsfeedback element and an estimated relevance element comprising anestimated relevance indicator based on the estimated relevance score,wherein the estimated relevance indicator provides a visual indicationof the degree to which the result set of documents are relevant to thesearch query; updating the results feedback element based on the resultset of documents; updating the estimated relevance element to displaythe estimated relevance indicator based on the estimated relevancescore; determining that the estimated relevance score is less than arelevance score threshold; and in response to determining that theestimated relevance score is less than the relevance score threshold,providing at least one suggestion for improving the search query. 11.The method of claim 10, wherein the estimated relevance indicatorprovides a visual indication that query improvement is suggested. 12.The method of claim 10, wherein providing at least one suggestion forimproving the search query includes providing for display one or moresuggested query terms.
 13. The method of claim 10, wherein providing atleast one suggestion for improving the search query includes providingfor display a prompt requesting an entry of at least one predefinedquery component.
 14. The method of claim 13, wherein the at least onequery component includes a jurisdiction, a date, a practice area, and acontent type.
 15. The method of claim 10, further comprising: inresponse to providing at least one suggestion for improving the searchquery, receiving a query improvement; in response to receiving the queryimprovement, generating an improved search query; searching the databaseusing the improved search query to identify an improved result set ofdocuments, wherein the improved result set of documents are identifiedbased on the improved search query; calculating an improved estimatedrelevance score for the improved result set of documents, wherein theimproved estimated relevance score is indicative of a degree to whichthe improved result set of documents are relevant to the improved searchquery; and providing for display an updated estimated relevanceindicator based on the improved estimated relevance score, wherein theupdated estimated relevance indicator provides a visual indication ofthe degree to which the improved result set of documents are relevant tothe improved search query.
 16. A method for displaying a visualizationof a search query, the method comprising: providing for display, by acomputer comprising a processor and a memory component, a graphical userinterface having a query input element; receiving a search query enteredinto the query input element, wherein the search query includes aplurality of query terms; providing for display on the graphical userinterface a query visualization and manipulation element including aplurality of nodes and a plurality of connectors, wherein the pluralityof nodes correspond to the plurality of query terms, wherein eachconnector of the plurality of connectors connects a pair of theplurality of nodes and is representative of a proximity of thecorresponding query terms of the connected pair of nodes; providing fordisplay an estimated relevance element and a results feedback element onthe graphical user interface; searching a database using the searchquery to identify a result set of documents, wherein the result set ofdocuments are identified based on the search query; calculating anestimated relevance score for the result set of documents, wherein theestimated relevance score is indicative of a degree to which the resultset of documents are relevant to the search query; updating the resultsfeedback element based on the result set of documents; and updating theestimated relevance element to display the estimated relevance indicatorbased on the estimated relevance score, wherein the estimated relevanceindicator provides a visual indication of the degree to which the resultset of documents are relevant to the search query.
 17. The method ofclaim 16, further comprising determining that the estimated relevancescore is less than a relevance score threshold; in response todetermining that the estimated relevance score is less than therelevance score threshold, updating the query visualization andmanipulation element to provide at least one suggestion for improvingthe search query; in response to providing at least one suggestion forimproving the search query, receiving a query improvement; in responseto receiving the query improvement, generating an improved search query;searching the database using the improved search query to identify animproved result set of documents, wherein the improved result set ofdocuments are identified based on the improved search query; calculatingan improved estimated relevance score for the improved result set ofdocuments, wherein the improved estimated relevance score is indicativeof a degree to which the improved result set of documents are relevantto the improved search query; updating the results feedback elementbased on the improved result set of documents; and updating theestimated relevance element to display an improved estimated relevanceindicator based on the improved estimated relevance score, wherein theimproved estimated relevance indicator provides a visual indication ofthe degree to which the improved result set of documents are relevant tothe improved search query.
 18. The method of claim 16, wherein theestimated relevance indicator is a bar, wherein a length of the bar isindicative of the degree to which the result set of documents arerelevant to the search query.
 19. The method of claim 16, wherein acolor of the estimated relevance indicator is indicative of the degreeto which the result set of documents are relevant to the search query.